Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

Abstract

We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed method detects faults with 97% accuracy, pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.

arXiv comment2021 European Conference on Optical Communication (ECOC)

References in corpus (2)

• Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory
• A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis

Cited by in corpus (3)

• Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
• Faulty Branch Identification in Passive Optical Networks using Machine Learning
• A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles